Generative AI makes it easier than ever to build applications. An idea can become a prototype within a short time – complete with a user interface, database and perhaps even a login. This allows business teams in particular to create solutions for problems that central IT may not have the time or resources to address.

But once other people start using an application, real company data is involved or business processes depend on it, a working prototype is no longer enough. The experiment has become software that needs to operate reliably.

When Experiments Go Live

Enabling business teams to build their own applications creates new opportunities. They often have the deepest understanding of their processes, requirements and everyday challenges. Generative AI and vibe coding lower the technical barriers and make it possible to test ideas quickly.

The transition to production software, however, is rarely clear-cut. A personal productivity tool can quickly become something an entire team relies on. At that point, familiar software engineering questions arise: Who can access which data? Who is responsible for maintenance and updates? And who takes care of operations?

A professional-looking interface does not answer these questions. Security, data protection and maintainability remain essential – regardless of whether the code was written by developers or generated with the help of AI.

The role of AI itself also matters. An application developed with AI is not necessarily an AI system. If the application uses an AI model at runtime or sends company data to an external AI service, additional requirements around governance, data protection and potentially regulation come into play.

Enable Innovation with Clear Guardrails

The answer is hardly to prevent experimentation or subject every small productivity tool to an extensive approval process. Instead, companies need a pragmatic path from experimentation to regular operations.

This includes approved tools, clear rules for handling company data and criteria for determining when an application requires professional review. As an application becomes more important, clear ownership of security, maintenance and operations needs to be established as well.

Conclusion

Generative AI can turn valuable process knowledge into working solutions much faster. But it does not make professional software development obsolete. Instead, the role of software engineering is evolving: helping turn successful experiments into secure, maintainable applications that can be operated reliably over the long term.

When business teams and software engineers manage this transition together, a quick prototype can become a dependable tool.

This article is based on the September edition of our “Schlicht und einfach” column in Inside IT. The original article by Markus Schlichting, CEO of Karakun, was adapted for karakun.com to explore the transition from prototypes to production software from a software engineering perspective.

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